“Ready to ship?” gets a thumbs-up emoji and a regret. A rollout risk review names blast radius, defines metrics, sets abort criteria, and confirms support / comms are ready — before any percentage is bumped.
Who this is for
Engineering managers, founders, PMs co-owning launches, anyone who has watched a 100% rollout turn into a 100% rollback.
When not to use these prompts
Don’t use this for changes hidden behind a paid setting that 10 customers use. Don’t use it on infra-only changes — different review (deploy + migration).
Prompt anatomy / structure formula
Every rollout risk prompt should carry six elements:
- Role: who AI plays (SRE / release captain / staff engineer / QA lead).
- Context: stack / branch / failing logs / diff / dashboard URL.
- Goal: one concrete deliverable — root cause, checklist, plan, ticket list, runbook.
- Constraints: what AI MUST NOT do (don’t auto-fix, don’t hallucinate file paths).
- Output format: numbered findings, markdown table, JSON, unified diff, runnable code.
- Examples / signal: 1-2 “good” output examples, or counter-examples.
Best for
- Pre-launch rollout plan
- Defining abort criteria
- Support / comms readiness
- A/B vs rollout decisioning
- Sunset rollout for an old feature
12 copy-ready prompt templates
1. Rollout risk triage
For feature `{featureName}`, assess: (1) Blast radius (% users / which segments), (2) Reversibility (instant flag flip / requires deploy / requires migration backtrack), (3) Detection lag (how long before we'd notice broken), (4) Support load impact. Output GREEN / YELLOW / RED.Variables to swap: featureName
2. Phased rollout plan
Design a phased rollout: (1) Internal / employees only, (2) 1% real users 24h, (3) 10% 24h, (4) 50% 48h, (5) 100%. For each phase: success metric, alert threshold, abort criteria, who decides to advance. Don't propose Day 1 = 100%.3. Abort criteria
Define abort criteria for this rollout: (1) Error rate threshold per endpoint, (2) Conversion drop threshold, (3) Support ticket spike threshold, (4) On-call paging count. Specify exact numbers, not "if it looks bad". Include who has authority to abort.4. Cohort / targeting plan
Choose target cohorts for the first rollout: (1) Internal employees, (2) Power users (engaged 30+ days), (3) New signups (less harm if broken), (4) Specific orgs we trust. Avoid: paid enterprise, accessibility-flagged users. Output cohort definition.5. A/B vs rollout decision
Should this be a rollout or an A/B test? Criteria: (a) Are we measuring uplift vs catching regressions? (b) Is sample size sufficient for an A/B? (c) Is the metric well-defined? (d) How long would we keep the variant arm alive? Recommend one, with reasoning.6. Pre-launch checklist
Generate a launch checklist: (1) Feature flag live in target env, (2) Dashboards exist with new metrics, (3) Alerts armed, (4) Rollback / kill-switch tested, (5) Support docs published, (6) Customer comms drafted, (7) On-call assigned. Each item GO / NO-GO.7. Support readiness
Audit support readiness: (1) Help-center article live, (2) Macro / canned reply created, (3) Internal Slack channel for escalation, (4) Known limitations documented. Output: missing items + owner.8. Comms plan
Draft comms: (1) In-product release note, (2) Email to existing affected users (if behaviour changes), (3) Tweet / LinkedIn for one user-visible benefit, (4) Status-page entry if any risk of degradation. Keep each ≤ 100 words.9. Sunset rollout
We're sunsetting feature `{old}`. Plan: (1) Deprecation banner at T-30, (2) Email at T-14, (3) Read-only at T-7, (4) Full removal at T. Per phase: comms + opt-out / migration path. Customers must hear about it 3 times via different channels.Variables to swap: old
10. Rollout post-mortem
Rollout {featureName} hit an abort criterion. Write a brief retro: (1) What broke, (2) Detection time, (3) Rollback time, (4) Why didn't earlier phase catch it, (5) One process change. ≤ 250 words. No blame.Variables to swap: featureName
11. Slow rollout for risk-averse customers
Some customers (enterprise / regulated) don't want auto-rollout. Design an opt-in path: (1) Default state, (2) UI to opt in, (3) How they roll back, (4) Comms cadence. Don't force changes on customers who told us not to.12. Re-rollout after rollback
We rolled back. Now we want to retry. Plan: (1) Root cause fixed + tested, (2) Phase plan restarted from 1%, (3) Anything we should monitor that we didn't before, (4) Comms — what to say to customers who saw the old version.Common mistakes
- Skipping a phased rollout because “the feature is small”.
- No abort criteria — you keep advancing because nobody wants to stop.
- Targeting paid customers first — high blast radius, high reputational cost.
- Forgetting support — they get blindsided.
- No reversibility check — you discover at 50% that you can’t go back.
- Conflating A/B test with rollout — measures different things.
- No comms — customers find out via Twitter.
How to push results further
- Start at 1% real users. Anything less misses signal.
- Abort criteria must be concrete numbers, not “if it feels bad”.
- Pair rollout with a paired metric (success + counter — engagement up but support load up = abort).
- Always test rollback before phase 1, not after phase 4.
- Comms = in-product + email + status page for anything user-visible.
- Sunset slowly with 3 touchpoints over 30 days.
- Retros after every aborted rollout — usually reveals a monitoring gap.
Practical depth notes
Use these prompts as starting points, not final answers. For Feature Rollout Risk Review Prompts: 12 Templates for Safe Launches, the useful extra work is to replace every generic placeholder with a real constraint: audience, channel, length, brand voice, examples to imitate, and examples to avoid. Run at least two versions with different constraints, then compare the outputs side by side instead of accepting the first polished response.
A good result should pass three checks: it is specific enough that another person could reuse it, it avoids vague praise or filler, and it gives you an editable artifact rather than a broad suggestion. If the output feels generic, add one concrete reference, one forbidden pattern, and one measurable success criterion before rerunning the prompt.
FAQ
- Does every feature need a phased rollout?: No — small UI tweaks, copy changes, no. Behaviour changes, anything touching auth / payments / data, yes.
- What’s a good first cohort?: Employees → power users → new signups → general → enterprise.
- A/B or rollout?: A/B when measuring uplift; rollout when catching regressions. They serve different goals.
- How fast should abort be?: < 5 min for code, < 30 min for data — same as deploys.
- How do I communicate sunsets?: 3 touchpoints over 30+ days: in-product banner, email, status page.
- When can I retire a feature flag?: After 30 days at 100% with no aborts. Otherwise it becomes its own debt.
Related
- Release checklist prompts
- Deployment check prompts
- Code review prompts
- PR review prompts
- Changelog Generation Prompts: 12 Templates for Useful Release Notes
- Coding & Developer Prompts hub
Tags: #Prompt #Coding #Release #Feature flag